Feedback Conditioning Layer:
The primary objective of this layer is the propagation of prior knowledge from previous feed-forward pass prediction to \(\mathbf {T}\) . This action couples the rich visual information extracted by \(\mathbf {T_{A}}\) , with the prior feedback signal coming from earlier prediction. A noteworthy mention in this context would be the work by Perez [1]}, where a general purpose conditioning layer FiLM has been designed based on simple feature-wise affine transformation operation for visual reasoning. Works involving visual-question-answering [1]}, image style transfer [3]} and semantic image synthesis [4]} have been seen to endorse a similar idea as well. Based on some prior or conditional information, the intermediate activation map \(\mathrm {\Psi }\)  could be modulated as: \(\mathrm {\hat{\Psi } = \Psi \odot \gamma + \omega }\) , where \(\mathrm {\gamma }\) , \(\mathrm {\omega }\)  are global affine transformation parameters. They both have dimensions \(\mathbb {R}^{\hat{T}}\) , and are usually predicted by a network receiving the conditional information as input. In lieu of transforming each channel globally, we allow local transformations [4]} that attune each spatial position of the activation map. Following earlier mathematical notations and assumptions, the feedback conditioning layer can be formulated as: \(\mathrm {\hat{\Psi } = \Psi \odot \Gamma + \Omega }\)  where \(\lbrace  \Gamma , \Omega \rbrace  \triangleq \mathcal {P}\)  are the local transformation parameters predicted by the feedback network having dimension of \(\mathbb {R}^{\hat{H} \times \hat{W} \times \hat{T}}\) , similar to \(\mathrm {\Psi }\) . This layer couples the prediction \(Y\)  with visual feature \(\mathrm {\Psi }\)  by scaling, shifting and ReLU influenced selective thresholding, alternatively. Compared to global tuning [1]}, such local harmonisation of features ensure a better fine-grained control over the activations at each layer.
